Balancing Accuracy and Fairness for Interactive Recommendation with Reinforcement Learning
This addresses fairness issues in dynamic recommendation systems, which is an incremental improvement over static fairness methods.
The paper tackles the problem of balancing accuracy and fairness in interactive recommender systems, where user preferences and fairness status change over time, and proposes FairRec, a reinforcement learning framework that improves fairness while preserving recommendation quality.
Fairness in recommendation has attracted increasing attention due to bias and discrimination possibly caused by traditional recommenders. In Interactive Recommender Systems (IRS), user preferences and the system's fairness status are constantly changing over time. Existing fairness-aware recommenders mainly consider fairness in static settings. Directly applying existing methods to IRS will result in poor recommendation. To resolve this problem, we propose a reinforcement learning based framework, FairRec, to dynamically maintain a long-term balance between accuracy and fairness in IRS. User preferences and the system's fairness status are jointly compressed into the state representation to generate recommendations. FairRec aims at maximizing our designed cumulative reward that combines accuracy and fairness. Extensive experiments validate that FairRec can improve fairness, while preserving good recommendation quality.